Library’s

library(flexdashboard)
library(DT)
library(ggplot2)
library(readxl)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(stringr)

Input Datasets and import

• GDP(GDP.xls) : GDP by country
• GDP Percapita(GDP per Capita.xls) : GDP Percapita by country
• Manufacturing GDP(Manufacturing.xls) : Manufacturing GDP percentage by country
• Agriculture GDP(Agriculture.xls) : Agriculture GDP percentage by country
• Service GDP(Service.xls) : Service GDP percentage by country
• Industries GDP(Industries.xls) : Industries GDP percentage by country
• Ores_Metals_Minerals GDP(Ores_Metals_Minerals.xls) : Ores_Metals_Minerals GDP percentage by country

Importing data into R

#disable scientific notation, so that actual decimal values are imported instead of exponential factors
options(scipen = 999)


# Importing country Metadta dataset into R
country_metadata_dataset <- read_excel("GDP.xls", col_names = TRUE, sheet = "Metadata - Countries")

# Importing GDP (1995-2018) by country dataset into R
gdp_dataset <- read_excel("GDP.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing GDP percapita (1995-2018) by country dataset into R
gdp_percapita_dataset <- read_excel("GDP per Capita.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing Manufacturing GDP (1995-2018) percentage by country dataset into R
gdp_manufacturing_dataset <- read_excel("Manufacturing.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing Agriculture GDP (1995-2018) percentage by country dataset into R
gdp_agriculture_dataset <- read_excel("Agriculture.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing Service GDP (1995-2018) percentage by country dataset into R
gdp_service_dataset <- read_excel("Service.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing Industries GDP (1995-2018) percentage by country dataset into R
gdp_industries_dataset <- read_excel("Industries.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

# Importing Ores_Metals_Minerals GDP (1995-2018) percentage by country dataset into R
gdp_ores_metals_minerals_dataset <- read_excel("Ores_Metals_Minerals.xls", col_names = TRUE, sheet = "Data", skip = 3) %>%
  data.frame(., stringsAsFactors = F) %>%
  select(., 1,2,3,40:63)

Merging Dataframes

• GDP is in millions
• Input datasets are merged to a single dataframe with all the relevant information

MergeDF

df1 <- gather(gdp_dataset, "year", "GDP", 4:27) %>% select(1, 4, 5)
df1$GDP <- df1$GDP/1000000
df2 <- gather(gdp_percapita_dataset, "year", "GDP Percapita", 4:27) %>% select(1, 4, 5)
df3 <- gather(gdp_industries_dataset, "year", "Industry Percent of GDP", 4:27) %>% select(1, 4, 5)
df4 <- gather(gdp_service_dataset, "year", "Services Percent of GDP", 4:27) %>% select(1, 4, 5)
df5 <- gather(gdp_agriculture_dataset, "year", "Agriculture Percent of GDP", 4:27) %>% select(1, 4, 5)
df6 <- gather(gdp_manufacturing_dataset, "year", "Manufacturing Percent of GDP", 4:27) %>% select(1, 4, 5)
df7 <- gather(gdp_ores_metals_minerals_dataset, "year", "Ores_Metals_Minerals Percent of GDP", 4:27) %>% select(1, 4, 5)
df <- merge(df1, df2, all.y = T)
df <- merge(df, df3, all.y = T)
df <- merge(df, df4, all.y = T)
df <- merge(df, df5, all.y = T)
df <- merge(df, df6, all.y = T)
df <- merge(df, df7, all.y = T)
df <- merge(country_metadata_dataset, df, by.x = "TableName", by.y = "Country.Name", all.y = T)

# removing characters from the year and converting the type to numeric
df$year <- str_extract(df$year, "[:digit:]+") %>%
  as.numeric(df$year)

Income groups Dataframe

• GDP is in millions

IncomeGroupsDF

incomegroup_df <- df %>%
  filter(., is.na(IncomeGroup)) %>%
  filter(., `Country Code` %in% c("EAR","FCS","HIC","HPC","LDC","LIC","LMC","LMY","LTE","MIC","PRE","PST","UMC")) %>%
  arrange(TableName, year)

Economy by Region Dataframe

• GDP is in millions
• Economy from major sectors(Services, Agriculture and Manufacturing) by region

RegionsDF

economy_by_region_df <- df %>%
  filter(., is.na(IncomeGroup)) %>%
  filter(., TableName %in% c("East Asia & Pacific","Europe & Central Asia","Latin America & Caribbean","Middle East & North Africa","North America","South Asia","Sub-Saharan Africa")) %>%
  select(1,3,4,6:8,10:13) %>%
  arrange(TableName, year)

Knowledge vs traditional GDP Dataframe

• GDP is in millions

KnowledgeTraditinoalDF

knowledge_traditinoal_dF <- df %>%
  filter(., !is.na(IncomeGroup)) %>%
  select(1,3,4,6,7,10:13) %>%
  mutate("Knowledge based Percent of GDP" = ifelse(is.na(`Services Percent of GDP`), 0, `Services Percent of GDP`)+
           ifelse(is.na(`Manufacturing Percent of GDP`),0,`Manufacturing Percent of GDP`),
         "Traditinoal based Percent of GDP" = ifelse(is.na(`Agriculture Percent of GDP`),0,`Agriculture Percent of GDP`)+
           ifelse(is.na(`Ores_Metals_Minerals Percent of GDP`),0,`Ores_Metals_Minerals Percent of GDP`)) %>%
  arrange(TableName, year)

country_gdp_mean_sd_dF <- knowledge_traditinoal_dF %>%
  group_by(TableName) %>%
  summarise("Country Mean GDP" = mean(GDP, na.rm=TRUE),
            "Country SD GDP" = sd(GDP, na.rm=TRUE)
            )

world_knowledge_gdp_percent_mean_dF <- knowledge_traditinoal_dF %>%
  group_by(year) %>%
  summarise("World Mean Knowledge GDP percent" = mean(`Knowledge based Percent of GDP`, na.rm=TRUE))

world_traditional_gdp_percent_mean_dF <- knowledge_traditinoal_dF %>%
  group_by(year) %>%
  summarise("World Mean Traditinoal GDP percent" = mean(`Traditinoal based Percent of GDP`, na.rm=TRUE))

knowledge_traditinoal_dF <- knowledge_traditinoal_dF %>%
  merge(., country_gdp_mean_sd_dF, by.x = "TableName", by.y = "TableName", all.y = T) %>%
  mutate("Country SD GDP in percent" = `Country SD GDP`/`Country Mean GDP`*100) %>%
  merge(., world_knowledge_gdp_percent_mean_dF, by.x = "year", by.y = "year", all.y = T) %>%
  merge(., world_traditional_gdp_percent_mean_dF, by.x = "year", by.y = "year", all.y = T) %>%
  select(1:5,10:16) %>%
  na_if(., 0) %>%
  arrange(TableName, year)

Data Frames for Dashboard

Column

initial dataframe

• GDP is in millions
• Input datasets are merged to a single dataframe with all the relevant information

### initial dataframe
DT::datatable(df, options = list(pageLength = 5))

### incomegroup dataframe
DT::datatable(incomegroup_df, options = list(pageLength = 5))

### economy by region dataframe
DT::datatable(economy_by_region_df, options = list(pageLength = 5))

### knowledge and traditinoal GDP's dataframe
DT::datatable(knowledge_traditinoal_dF, options = list(pageLength = 5))

Income groups Charts

column

Number of Incomegroups

NIG <- length(unique(incomegroup_df[["TableName"]]))

valueBox(NIG, color = "primary")

Column

GDP

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `GDP`)) + 
  geom_line(aes(y = `GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "GDP by income groups in millions")

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_path).

GDP Percapita

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `GDP Percapita`)) + 
  geom_line(aes(y = `GDP Percapita`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "GDP Percapita by income groups")

Industry Percent of GDP

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Industry Percent of GDP`)) + 
  geom_line(aes(y = `Industry Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Industry Percent of GDP by income groups")

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_path).

Services Percent of GDP

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Services Percent of GDP`)) + 
  geom_line(aes(y = `Services Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Services Percent of GDP by income groups")

## Warning: Removed 38 rows containing missing values (geom_point).

## Warning: Removed 38 rows containing missing values (geom_path).

Agriculture Percent of GDP

ggplot(incomegroup_df, aes(x=year, colour=TableName, group = TableName)) +
  geom_point(aes(y = `Agriculture Percent of GDP`)) + 
  geom_line(aes(y = `Agriculture Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Agriculture Percent of GDP by income groups")

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_path).

Manufacturing Percent of GDP

## Warning: Removed 52 rows containing missing values (geom_point).

## Warning: Removed 49 rows containing missing values (geom_path).

Ores_Metals_Minerals Percent of GDP

ggplot(incomegroup_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Ores_Metals_Minerals Percent of GDP`)) + 
  geom_line(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Ores_Metals_Minerals Percent of GDP by income groups")

## Warning: Removed 84 rows containing missing values (geom_point).

## Warning: Removed 77 rows containing missing values (geom_path).

Economy by region Charts

column

Number of Regions

NIG <- length(unique(economy_by_region_df[["TableName"]]))

valueBox(NIG, color = "primary")

Service based economy

valueBox("North America", color = "info")

### Manufacturing based economy
valueBox("East Asia and Pacific", color = "info")

### Agriculture based economy

valueBox("South Asia", color = "info")

Column

### GDP

ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `GDP`)) + 
  geom_line(aes(y = `GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "GDP by Region in millions")

### GDP Percapita

ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `GDP Percapita`)) + 
  geom_line(aes(y = `GDP Percapita`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "GDP Percapita by Region")

### Services Percent of GDP

ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Services Percent of GDP`)) + 
  geom_line(aes(y = `Services Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Services Percent of GDP by Region")

## Warning: Removed 14 rows containing missing values (geom_point).

## Warning: Removed 14 rows containing missing values (geom_path).

### Agriculture Percent of GDP

ggplot(economy_by_region_df, aes(x=year, colour=TableName, group = TableName)) +
  geom_point(aes(y = `Agriculture Percent of GDP`)) + 
  geom_line(aes(y = `Agriculture Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Agriculture Percent of GDP by Region")

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_path).

### Manufacturing Percent of GDP

ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Manufacturing Percent of GDP`)) + 
  geom_line(aes(y = `Manufacturing Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Manufacturing Percent of GDP by Region")

## Warning: Removed 14 rows containing missing values (geom_point).

## Warning: Removed 14 rows containing missing values (geom_path).

### Ores_Metals_minerals Percent of GDP

ggplot(economy_by_region_df, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Ores_Metals_Minerals Percent of GDP`)) + 
  geom_line(aes(y = `Ores_Metals_Minerals Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Ores_Metals_minerals Percent of GDP by Region")

## Warning: Removed 12 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_path).

Knowledge vs traditinoal GDP

knowledge_traditinoal_SD_1to25percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` <= 25) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_25to35percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 25 & `Country SD GDP in percent` <=35) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_35to45percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 35 & `Country SD GDP in percent` <=45) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_45to55percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 45 & `Country SD GDP in percent` <=55) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_55to65percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 55 & `Country SD GDP in percent` <=65) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_65to75percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 65 & `Country SD GDP in percent` <=75) %>%
  arrange(TableName, year)

knowledge_traditinoal_SD_75to100percent_dF <- knowledge_traditinoal_dF %>%
  filter(.,`Country SD GDP in percent` > 75) %>%
  arrange(TableName, year)

SD_Groups <- c("knowledge_traditinoal_SD_1to25percent_dF","knowledge_traditinoal_SD_25to35percent_dF","knowledge_traditinoal_SD_35to45percent_dF","knowledge_traditinoal_SD_45to55percent_dF","knowledge_traditinoal_SD_55to65percent_dF","knowledge_traditinoal_SD_65to75percent_dF","knowledge_traditinoal_SD_75to100percent_dF")

column

Number of SD percent groups

NSDG <- length(SD_Groups)

valueBox(NSDG, color = "info")

### Recessions

valueBox("2001, 2008-2009", color = "info")

column

World chart

ggplot(knowledge_traditinoal_dF, aes(x=factor(year), group = 1)) +
  geom_point(aes(y = `World Mean Knowledge GDP percent`)) + 
  geom_line(aes(y = `World Mean Knowledge GDP percent`, colour = "1")) +
  geom_point(aes(y = `World Mean Traditinoal GDP percent`)) + 
  geom_line(aes(y = `World Mean Traditinoal GDP percent`, colour = "2")) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "World knowledge based GDP percent mean") +
  xlab("year") +
  ylab("World mean knowledge based GDP percent") +
  scale_color_discrete(name = "GDP category", labels = c("World Mean Knowledge GDP percent", "World Mean Traditinoal GDP percent"))

### SD upto 25 percent

ggplot(knowledge_traditinoal_SD_1to25percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.position = "bottom", legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 213 rows containing missing values (geom_point).

## Warning: Removed 213 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_1to25percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 163 rows containing missing values (geom_point).

## Warning: Removed 162 rows containing missing values (geom_path).

### SD >25 and upto 35 percent

ggplot(knowledge_traditinoal_SD_25to35percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 124 rows containing missing values (geom_point).

## Warning: Removed 124 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_25to35percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 95 rows containing missing values (geom_point).

## Warning: Removed 95 rows containing missing values (geom_path).

### SD >35 upto 45 percent

ggplot(knowledge_traditinoal_SD_35to45percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 145 rows containing missing values (geom_point).

## Warning: Removed 142 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_35to45percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 98 rows containing missing values (geom_point).

## Warning: Removed 95 rows containing missing values (geom_path).

### SD >45 upto 55 percent

ggplot(knowledge_traditinoal_SD_45to55percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 79 rows containing missing values (geom_point).

## Warning: Removed 79 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_45to55percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 48 rows containing missing values (geom_point).

## Warning: Removed 42 rows containing missing values (geom_path).

### SD >55 upto 65 percent

ggplot(knowledge_traditinoal_SD_55to65percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 61 rows containing missing values (geom_point).

## Warning: Removed 51 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_55to65percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 33 rows containing missing values (geom_point).

## Warning: Removed 29 rows containing missing values (geom_path).

### SD >65 upto 75 percent

ggplot(knowledge_traditinoal_SD_65to75percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.text = element_text(size=6), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 14 rows containing missing values (geom_point).

## Warning: Removed 13 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_65to75percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 13 rows containing missing values (geom_point).

## Warning: Removed 12 rows containing missing values (geom_path).

### SD >75 percent

ggplot(knowledge_traditinoal_SD_75to100percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Knowledge based Percent of GDP`)) + 
  geom_line(aes(y = `Knowledge based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Knowledge based Percent of GDP by income groups in millions") +
  theme(legend.position = "bottom", legend.text = element_text(size=8), legend.margin = margin(t = 0, unit='cm'))

## Warning: Removed 36 rows containing missing values (geom_point).

## Warning: Removed 36 rows containing missing values (geom_path).

ggplot(knowledge_traditinoal_SD_75to100percent_dF, aes(x=factor(year), colour=TableName, group = TableName)) +
  geom_point(aes(y = `Traditinoal based Percent of GDP`)) + 
  geom_line(aes(y = `Traditinoal based Percent of GDP`)) +
  theme(axis.text.x = element_text(size=10, angle=90)) +
  theme(axis.text.y = element_text(size=10, angle=90)) +
  labs(title = "Traditional based Percent of GDP by income groups in millions") +
  theme(legend.position = "none")

## Warning: Removed 22 rows containing missing values (geom_point).

## Warning: Removed 17 rows containing missing values (geom_path).